agents-flex

agents-flex

Agents-Flex is a Java-native, lightweight and high-performance AI agent framework that unifies model calling, tool calling, RAG, MCP, Skills, Text2SQL, LLM Wiki and observability to help Java teams build production-grade AI applications.

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Quick Overview

Best for: AI Agents

What it does

AI Agents software for decision-makers comparing workflow fit and alternatives.

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agents-flex

Agents-Flex is a Java-native AI agent framework designed for Java developers to integrate models, tools, and knowledge into production-grade AI applications. It provides unified model abstractions (ChatModel, EmbeddingModel, ImageModel, RerankModel), tool calling, RAG and structured-knowledge capabilities (including LLM Wiki), multi-agent orchestration, and observability features. The project targets production readiness with Spring Boot integration, routing, retries, circuit-breaker patterns and OpenTelemetry support, and is released under the Apache-2.0 license. Agents-Flex requires Java 8+ and offers a modular ecosystem of more than 20 modules for assembling needed capabilities.

Java-based LLM application framework similar to LangChain.

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Key Features

Unified Model Abstraction

Provides interfaces such as ChatModel, EmbeddingModel, ImageModel and RerankModel to connect to multiple model providers (OpenAI, Qwen, DeepSeek, Ollama and private/on-prem model services).

Synchronous and Streaming Responses

Supports synchronous and stream-style responses over HTTP, SSE and WebSocket to handle different model response modes.

Agent Tools and Orchestration

Transforms Java methods into callable tools via annotations or builders, supports MCP tools, Skills, ReAct, Routing and Subagent patterns to let agents call business capabilities and decompose complex tasks.

Knowledge & RAG Support

Includes pipelines for document processing, embedding, vector storage, retrieval, reranking and an LLM Wiki to combine flat semantic search with hierarchical knowledge navigation.

Production-Grade Reliability & Observability

Provides features aimed at real services: high-availability routing, load balancing, retry and circuit-breaker mechanisms, call-tracing and metric collection, plus Spring Boot automatic configuration and OpenTelemetry integration.

Modular Ecosystem

Modular architecture with 20+ modules (ChatTool, MCP, Skills, Subagent, Text2SQL, WebSearch, LLM Wiki, Vector Store, Embedding, Rerank, Observability) so teams can pick and combine functionality as needed.

Pricing

Free Tier Available

Open-source under the Apache-2.0 license (no commercial pricing disclosed on site).

Use Cases

Intelligent customer service and chat assistants

Build integrated chat assistants that leverage models, tools and knowledge to answer user queries in production.

Enterprise knowledge base and RAG Q&A

Combine document processing, embeddings and retrieval to provide RAG-enabled question answering over corporate data.

Intelligent data analysis and natural language queries

Use Text2SQL and model capabilities to support natural-language data analysis and automated data queries.

MCP tool connectivity and automation

Expose business methods as tools to automate workflows and integrate multi-capability processes.

Hierarchical document navigation and LLM Wiki

Support multi-level knowledge navigation and an LLM Wiki for structured knowledge retrieval and exploration.

Multi-model gateway and high-availability routing

Act as a gateway to multiple models with routing and high-availability strategies for production deployments.

Integrations

OpenAI, Qwen, DeepSeek, Ollama

Model provider integrations supported via the unified model interfaces to call hosted or private models.

Private / On-prem model services

Supports connecting to self-hosted or private model services for deployments requiring on-prem models.

Vector stores and embedding pipelines

Integrates with embedding, vector storage and search components for RAG and semantic retrieval.

WebSearch and LLM Wiki

Integrates web search and an LLM Wiki component to enrich agent responses with external and structured knowledge.

Spring Boot

Provides Spring Boot starter and automatic configuration to simplify integration into Spring-based applications.

OpenTelemetry

Observability integration for tracing and metrics collection in production environments.

Benefits

Unifies models, tools, knowledge and observability into a single Java-native framework to accelerate production AI development.
Provides production-ready features (routing, retries, circuit-breaker, tracing, metrics, Spring Boot integration) for stable service operation.
Modular design lets teams incrementally adopt capabilities (models, tools, RAG, orchestration) without rewriting application structure.

Limitations

Requires Java 8+ runtime (framework is Java-native).
Positioned as a production-ready, integratable Java framework rather than a demo-only single-turn chat example.

Frequently Asked Questions

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Getting Started

  1. 1 Step 1: Add Agents-Flex as a dependency (Maven) and ensure Java 8+ runtime.
  2. 2 Step 2: Configure a ChatModel (example: OpenAI/Qwen provider) to start unified model calls.
  3. 3 Step 3: Expose business methods as Agent-callable tools using annotations or the Tool.Builder.
  4. 4 Step 4: Integrate knowledge sources (RAG, WebSearch, LLM Wiki) for external context.
  5. 5 Step 5: Orchestrate agent behavior with ReAct, Routing and Subagent for multi-step tasks.
  6. 6 Step 6: Enable production observability and resilience (routing, retry, circuit-breaker, OpenTelemetry, Spring Boot auto-config).

Support

docs

Help documentation and quick-start materials accessible from the Agents-Flex website.

repository

Gitee repository and project pages linked from the site for source, changelog and contributions.

API

Available: No

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